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knitr::opts_chunk$set(fig.width = 6, fig.height = 4, fig.path = 'Figs/',
                      echo = FALSE, message = FALSE, warning = FALSE)

dir_git     <- path.expand('~/github/ohibc')
source(file.path(dir_git, 'src/R/common.R'))  
dir_spatial <- file.path(dir_git, 'prep/_spatial')  
dir_anx     <- file.path(dir_M, 'git-annex/bcprep')


### goal specific folders and info
goal      <- 'fis'
scenario  <- 'v2017'
dir_goal  <- file.path(dir_git, 'prep', goal, scenario)
dir_goal_anx <- file.path(dir_anx, goal, scenario)

### provenance tracking
library(provRmd); prov_setup()

### Kobe plot functions
source(file.path(dir_goal, 'kobe_fxns.R'))
library(plotly)

There are 12 RAM stocks used for the FIS sub-goal.

1 RAM metrics for each stock

2 Catch over time for each stock

The catch values here come from the RAM database as well.

2.1 Plotting fishery catch weighting by region

THIS HAS NOT BEEN UPDATED FOR THE NEWEST RAM DATA

2.2 Contribution of regional catch by stock

Breaking this up by region and stock

3 B/Bmsy estimates from the global CMSY data

Pulling in the B/Bmsy estimates for stocks in FAO area 67 from this year (2017) for consideration in the model.

## [1] 151081.3

How much catch is reported for BC stocks in RAM? And of that catch, how much do these stocks contribute to the total?

Plot total catch assessed vs unassessed in a stacked bar chart

3.0.1 THE REST OF THIS IS OLD - NOT RUN. SHOULD PROBABLY DELETE

Let’s look at how catch changes by species over time using the SAUP data.

Match SAUP data to RAM data to get a better picture of how much catch is assessed vs not assessed

Change to catch prop